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Poor representations of aerosols, clouds and aerosol-cloud interactions (ACI) in Earth System Models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and key to these efforts is evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they are lack of the process-level understanding and representations of aerosols, clouds, and ACI. In this study, we developed an ESM aerosol-cloud diagnostics package (ESMAC Diags) to facilitate routine evaluation of aerosols, clouds and aerosol-cloud interactions simulated by the Department of Energy's (DOE) Energy Exascale Earth System Model (E3SM). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates from its version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in-situ and remote-sensing measurements from aircraft, ship, surface and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: Eastern North Atlantic, Central U.S., Northeastern Pacific and Southern Ocean, where frequent liquid or mixed-phase clouds are present and extensive measurements are available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms and heatmaps, to evaluate model representation of aerosols, clouds, and aerosol-cloud interactions. Select examples highlighting ESMAC Diags capabilities are shown using E3SM version 2 (E3SMv2). E3SMv2 in general can reasonably reproduces many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path adjustment to perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants a further study to improve the cloud microphysics parameterizations in E3SMv2. [ABSTRACT FROM AUTHOR]

Abstract: Air pollution caused by biomass burning has been a major concern over east China in recent decades. The distinct radiative effects of biomass burning aerosol due to its strong absorption also bring great uncertainty to the regional climate change. Therefore, it is urgent to investigate the spatial, temporal and vertical distribution of biomass burning aerosols over eastern China. In this paper, machine learning and multi-threshold method were used to identify biomass burning aerosol. We used a random forests model to identify biomass burning aerosols based on multi-source data includes Himawari-8 with high temporal resolution. The validated method would provide a basis for the study of aerosol radiation effect in China. This study is also useful to monitor smoke pollution in eastern China. [ABSTRACT FROM AUTHOR]